I think you're spot on with the observation that 2012 refers to when VCs discovered machine learning. Anyone who has recently interacted with VCs will tell you that they look for anything to do with machine learning (and VR/AR/MR), even when it makes no sense. There are going to be some companies who will be able to leverage machine learning to advance their business, namely, Google/Facebook who will probably claim they can offer better targeted advertising and such. Most other players who merely try to force machine learning on other fields are likely to realize that while the technology is cool, it's still too early for it to be generally applicable to "any" problem.
Especially dangerous is going to be the mix of machine learning with healthcare. I believe Theranos tried it and found out it's not that easy... I'd watch this space with skepticism.
> Especially dangerous is going to be the mix of machine learning with healthcare.
Medical diagnosis has been one of the primary application areas of AI since the 70s (maybe earlier, I can't remember off the top of my head). The widespread non-availability of automatic doctors should tell you how well that has worked :-(.
Coincidentally enough I worked both in AI (1980s) and drug development (2000s) and now really understand how hard it is!
I do believe we will soon see automated radiology analysis as it is likely to appear to be most amenable to automated analysis. Presumably in Asia first as the US FDA will justifiably require a lot of validation. The opportunity for silent defect is quite high -- you are right to say "especially dangerous"
Especially dangerous is going to be the mix of machine learning with healthcare. I believe Theranos tried it and found out it's not that easy... I'd watch this space with skepticism.